Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography.

Journal: Abdominal radiology (New York)
PMID: 34286371

Abstract

PURPOSE: In this study, we developed radiomic models that utilize a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from normal endometrium on routine computed tomography (CT).

Authors

  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.
  • Rong Hu
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Huizhou Li
    Department of Radiology, Second Xiangya Hospital, Changsha, China.
  • Yeyu Cai
    Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, China.
  • Paul J Zhang
    Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Chengzhang Zhu
    School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.

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